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Feature Selection Methods for Identifying Genetic Determinants of Host Species in RNA Viruses

Overview of attention for article published in PLoS Computational Biology, October 2013
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Title
Feature Selection Methods for Identifying Genetic Determinants of Host Species in RNA Viruses
Published in
PLoS Computational Biology, October 2013
DOI 10.1371/journal.pcbi.1003254
Pubmed ID
Authors

Ricardo Aguas, Neil M. Ferguson

Abstract

Despite environmental, social and ecological dependencies, emergence of zoonotic viruses in human populations is clearly also affected by genetic factors which determine cross-species transmission potential. RNA viruses pose an interesting case study given their mutation rates are orders of magnitude higher than any other pathogen--as reflected by the recent emergence of SARS and Influenza for example. Here, we show how feature selection techniques can be used to reliably classify viral sequences by host species, and to identify the crucial minority of host-specific sites in pathogen genomic data. The variability in alleles at those sites can be translated into prediction probabilities that a particular pathogen isolate is adapted to a given host. We illustrate the power of these methods by: 1) identifying the sites explaining SARS coronavirus differences between human, bat and palm civet samples; 2) showing how cross species jumps of rabies virus among bat populations can be readily identified; and 3) de novo identification of likely functional influenza host discriminant markers.

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Mendeley readers

The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 4 4%
United States 3 3%
Chile 1 <1%
Japan 1 <1%
Canada 1 <1%
Unknown 100 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 26%
Student > Ph. D. Student 20 18%
Student > Master 11 10%
Professor > Associate Professor 8 7%
Other 8 7%
Other 22 20%
Unknown 12 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 32%
Biochemistry, Genetics and Molecular Biology 12 11%
Medicine and Dentistry 11 10%
Computer Science 10 9%
Immunology and Microbiology 5 5%
Other 14 13%
Unknown 23 21%